Our ultimate objective is to predict, detect, and explain microbial adaptation, viewed as a evolutionary phenomenon, since evolution as a major factor in the emergence and spread of disease as well as in the large scale effectiveness of many treatments. This project will move towards this objective by meeting the following specific aims: 1) Develop new algorithms for aligning multiple sequences and inferring phylogenies, detecting recombinations, and aligning sequences; 3) Apply mathematical models of spatial organismal interaction and sequence loci interactions to evolutionary data from controlled experiments. Our new algorithms include: an iterative approach to discovering subtle similarities in subsequences with which to guide the full alignment; and a genetic algorithms to guide a progressive dynamic programming alignment. The phylogenetic inferencing algorithms using genetic algorithms to search the vast space of possible trees more efficiently. We evaluate representative current algorithms. as well as our own, using data from experimental evolution in other projects, and powerful statistical techniques for modeling recombination events. For the first time, we apply mathematical tools to spatial constraints in viral evolution, and we use mathematical tools from the theory of evolutionary computation to investigate the emergence. of correlated motifs in protein evolution and in abstract evolutionary processes.